Medical Image Analysis
At Microsoft Research Cambridge we are working on new technology to enable automatic and semiautomatic analysis of n-dimensional medical images. The results of our research will be of great help in measuring anomalies, detecting possible tumors and increasing the efficiency and accuracy of radiologists and clinicians. Ultimatelly, patients all over the globe will benefit from this technology.
Demos
(click on images to view demo videos)
> Automatic detection and localization of anatomical structures
Automatic semantic parsing of 3D medical scans
> Interactive segmentation of 3D medical images
Interactive segmentation of 3D anatomical structures
-Segmentation process
-Segmented aneurysm
-Segmented teeth
-Segmented aorta I
-Segmented aorta II
-Segmented head
-Segmented heart
-Segmented carotids
-Segmented lesion I
> Volumetric rendering on the GPU
Efficient volumetric rendering of medical images on the graphics processor
-visualization video 2
-visualization video 3
Images
Please click here to download some high-resolution snapshots.
Products
We are working together with the Microsoft Amalga team.
Publications
2009
- Antonio Criminisi, Toby Sharp, and Khan Siddiqui, Interactive Geodesic Segmentation of n-Dimensional Medical Images on the Graphics Processor, in Radiological Society of North America (RSNA), December 2009
- Antonio Criminisi, Jamie Shotton, Stefano Bucciarelli, and Khan Siddiqui, Automatic Semantic Parsing of CT Scans via Multiple Randomized Decision Trees, in Radiological Society of North America (RSNA), December 2009
- Victor Lempitsky, Michael Verhoek, Alison Noble, and Andrew Blake, Random Forest Classification for Automatic Delineation of Myocardium in Real-time 3D Echocardiography, in FIMH 2009 [best paper award], Springer Verlag, June 2009
- Victor Lempitsky, Surface Extraction from Binary Volumes with Higher-Order Smoothness, no. MSR-TR-2009-31, March 2009
- Antonio Criminisi, Jamie Shotton, and Stefano Bucciarelli, Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes, in MICCAI workshop on Probabilistic Models for Medical Image Analysis (MICCAI-PMMIA), 2009
- Zhao Yi, Antonio Criminisi, Jamie Shotton, and Andrew Blake, Discriminative, Semantic Segmentation of Brain Tissue in MR Images, in MICCAI 2009, Springer Verlag, 2009
2008
- Antonio Criminisi, Toby Sharp, and Andrew Blake, GeoS: Geodesic Image Segmentation, in Proc. European Conference on Computer Vision (ECCV), Springer, 2008
